class_docstr: Transformer with Masked Language Model
docstr: On Epoch Start
docstr_tok: ["On","Epoch","Start"]
code: def train_epoch_start(self, *args):
      """On Epoch Start"""
      self.last_global_step = self.global_step
      seed_everything(self.hparams.seed + self.trainer.current_epoch)
  
imports: from abc import ABC
  from base64 import decode
  from math import gamma
  import torch
  import torch.nn
  import torch.nn.functional
  import torchmetrics
  import pickle
  import pytorch_lightning
  from pytorch_lightning import seed_everything
  import os
  from pathlib import Path
  import logging
  from src.transformer.transformer_utils import *
  from src.transformer.transformer import Transformer, MaskedLanguageModel, CLS_Decoder
label_desc: Use image node_id 5 for calling the TransformerEncoder obj's underlying member method code with example usage: obj.train_epoch_start() without return types